PROJECT SUMMARY Meningiomas, which arise from arachnoid cells, make up >1/3 of all intracranial tumors. While typically benign, these tumors induce clinical symptoms due to mass effect and peritumoral edema. In cases requiring intervention, gross total resection provides the best outcomes when possible. However, treatment strategy is ultimately decided by determining the proper balance between surgical difficulty and the patient's overall health. Two mechanical properties are important predictors of surgical difficulty: tumor stiffness and adherence to surrounding tissues. Knowledge of these properties before surgery would allow clinicians to more accurately assess surgical risk and identify the most effective treatment strategy. Mechanical properties are difficult to predict by conventional imaging approaches, but can be directly assessed by Magnetic Resonance Elastography (MRE) and related Slip Interface Imaging (SII). In published studies, we have shown that MRE-based stiffness estimates are significantly correlated with tumor stiffness in meningiomas and pituitary adenomas. Furthermore, SII accurately predicted tumor adherence in meningiomas and vestibular schwannomas. Still, challenges remain to make these findings clinically-impactful. For estimating stiffness, the primary limitation lies in resolution. Therefore, in Aim 1 we will develop a voxel- wise classifier of tumor stiffness. This aim will build on our recently published neural network-based inversion (NNI), which has demonstrated superior performance to conventional direct inversions in simulation and in the brain. In Aim 1a, we will advance NNI by implementing more complex neural network architectures and creating more realistic simulations for training. In Aim 1b, the advances will be validated in a phantom with inhomogeneous stiffness. Finally, in Aim 1c with the aid of our Neurosurgery collaborators, we will collect a large sample of surgical stiffness assessments. We will use these assessments to train a voxel-wise stiffness classifier, which will then be validated in a separate test set. This aim will result in a map that conveys both stiffness and confidence in the prediction on a scale that is clinically meaningful to surgeons. The most-pressing limitations in SII include the subjective interpretation of the images and the lack of spatially resolved predictions. Aim 2 will address these challenges by developing a voxel-wise slip interface classifier. In Aim 2a, we will investigate a neural network-based predictor of slip interfaces to add to our current methods. In Aim 2b, we will evaluate if this new method can improve predictions in phantom experiments. In Aim 2c, we will again leverage surgical assessments of meningioma adherence to train and test a voxel-wise classifier. The result of this aim will be a map of tumor adherence represented as an easily interpreted probability. Taken together, these aims will provide neurosurgeons with clinically-important information to improve patient management. More broadly, technical advances made in this project will impact the entire MRE field.